FastDeploy/paddle2onnx/converter.cc

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "paddle2onnx/converter.h"

#include <fstream>
#include <iostream>
#include <set>
#include <string>

#include "paddle2onnx/mapper/exporter.h"
#include "paddle2onnx/optimizer/convert_fp32_to_fp16.h"

namespace paddle2onnx {

PADDLE2ONNX_DECL bool IsExportable(const char* model, const char* params,
                                   int32_t opset_version,
                                   bool auto_upgrade_opset, bool verbose,
                                   bool enable_onnx_checker,
                                   bool enable_experimental_op,
                                   bool enable_optimize, CustomOp* ops,
                                   int op_count, const char* deploy_backend) {
  auto parser = PaddleParser();
  if (!parser.Init(model, params)) {
    return false;
  }
  paddle2onnx::ModelExporter me;
  std::set<std::string> unsupported_ops;
  if (!me.CheckIfOpSupported(parser, &unsupported_ops,
                             enable_experimental_op)) {
    return false;
  }

  // Add custom operator information
  if (ops != nullptr && op_count > 0) {
    for (int i = 0; i < op_count; ++i) {
      std::string op_name(ops[i].op_name, strlen(ops[i].op_name));
      std::string export_op_name(ops[i].export_op_name,
                                 strlen(ops[i].export_op_name));
      if (export_op_name == "paddle2onnx_null") {
        export_op_name = op_name;
      }
      me.custom_ops[op_name] = export_op_name;
    }
  }

  if (me.GetMinOpset(parser, false) < 0) {
    return false;
  }
  std::string calibration_str;
  std::string onnx_model =
      me.Run(parser, opset_version, auto_upgrade_opset, verbose,
             enable_onnx_checker, enable_experimental_op, enable_optimize,
             deploy_backend, &calibration_str);
  if (onnx_model.empty()) {
    P2OLogger(verbose) << "The exported ONNX model is invalid!" << std::endl;
    return false;
  }
  if (parser.is_quantized_model && "tensorrt" == std::string(deploy_backend) &&
      calibration_str.empty()) {
    P2OLogger(verbose) << "Can not generate calibration cache for TensorRT "
                          "deploy backend when export quantize model."
                       << std::endl;
    return false;
  }
  return true;
}

PADDLE2ONNX_DECL bool IsExportable(const void* model_buffer, int model_size,
                                   const void* params_buffer, int params_size,
                                   int32_t opset_version,
                                   bool auto_upgrade_opset, bool verbose,
                                   bool enable_onnx_checker,
                                   bool enable_experimental_op,
                                   bool enable_optimize, CustomOp* ops,
                                   int op_count, const char* deploy_backend) {
  auto parser = PaddleParser();
  if (!parser.Init(model_buffer, model_size, params_buffer, params_size)) {
    return false;
  }
  paddle2onnx::ModelExporter me;
  std::set<std::string> unsupported_ops;
  if (!me.CheckIfOpSupported(parser, &unsupported_ops,
                             enable_experimental_op)) {
    return false;
  }

  // Add custom operator information
  if (ops != nullptr && op_count > 0) {
    for (int i = 0; i < op_count; ++i) {
      std::string op_name(ops[i].op_name, strlen(ops[i].op_name));
      std::string export_op_name(ops[i].export_op_name,
                                 strlen(ops[i].export_op_name));
      if (export_op_name == "paddle2onnx_null") {
        export_op_name = op_name;
      }
      me.custom_ops[op_name] = export_op_name;
    }
  }

  if (me.GetMinOpset(parser, false) < 0) {
    return false;
  }
  std::string calibration_str;
  std::string onnx_model =
      me.Run(parser, opset_version, auto_upgrade_opset, verbose,
             enable_onnx_checker, enable_experimental_op, enable_optimize,
             deploy_backend, &calibration_str);
  if (onnx_model.empty()) {
    P2OLogger(verbose) << "The exported ONNX model is invalid!" << std::endl;
    return false;
  }
  if (parser.is_quantized_model && "tensorrt" == std::string(deploy_backend) &&
      calibration_str.empty()) {
    P2OLogger(verbose) << "Can not generate calibration cache for TensorRT "
                          "deploy backend when export quantize model."
                       << std::endl;
    return false;
  }
  return true;
}

PADDLE2ONNX_DECL bool Export(
    const char* model, const char* params, char** out, int* out_size,
    int32_t opset_version, bool auto_upgrade_opset, bool verbose,
    bool enable_onnx_checker, bool enable_experimental_op, bool enable_optimize,
    CustomOp* ops, int op_count, const char* deploy_backend,
    char** calibration_cache, int* calibration_size, const char* external_file,
    bool* save_external, bool export_fp16_model) {
  auto parser = PaddleParser();
  P2OLogger(verbose) << "Start to parsing Paddle model..." << std::endl;
  if (!parser.Init(model, params)) {
    P2OLogger(verbose) << "Paddle model parsing failed." << std::endl;
    return false;
  }
  paddle2onnx::ModelExporter me;

  // Add custom operator information
  if (ops != nullptr && op_count > 0) {
    for (int i = 0; i < op_count; ++i) {
      std::string op_name(ops[i].op_name, strlen(ops[i].op_name));
      std::string export_op_name(ops[i].export_op_name,
                                 strlen(ops[i].export_op_name));
      if (export_op_name == "paddle2onnx_null") {
        export_op_name = op_name;
      }
      me.custom_ops[op_name] = export_op_name;
    }
  }

  std::string calibration_str;
  std::string result = me.Run(
      parser, opset_version, auto_upgrade_opset, verbose, enable_onnx_checker,
      enable_experimental_op, enable_optimize, deploy_backend, &calibration_str,
      external_file, save_external, export_fp16_model);
  if (result.empty()) {
    P2OLogger(verbose) << "The exported ONNX model is invalid!" << std::endl;
    return false;
  }
  if (parser.is_quantized_model && "tensorrt" == std::string(deploy_backend) &&
      calibration_str.empty()) {
    P2OLogger(verbose) << "Can not generate calibration cache for TensorRT "
                          "deploy backend when export quantize model."
                       << std::endl;
    return false;
  }
  *out_size = result.size();
  *out = new char[*out_size]();
  memcpy(*out, result.data(), *out_size);
  if (calibration_str.size()) {
    *calibration_size = calibration_str.size();
    *calibration_cache = new char[*calibration_size]();
    memcpy(*calibration_cache, calibration_str.data(), *calibration_size);
  }
  return true;
}

PADDLE2ONNX_DECL bool Export(
    const void* model_buffer, int64_t model_size, const void* params_buffer,
    int64_t params_size, char** out, int* out_size, int32_t opset_version,
    bool auto_upgrade_opset, bool verbose, bool enable_onnx_checker,
    bool enable_experimental_op, bool enable_optimize, CustomOp* ops,
    int op_count, const char* deploy_backend, char** calibration_cache,
    int* calibration_size, const char* external_file, bool* save_external,
    bool export_fp16_model) {
  auto parser = PaddleParser();
  P2OLogger(verbose) << "Start to parsing Paddle model..." << std::endl;
  if (!parser.Init(model_buffer, model_size, params_buffer, params_size)) {
    P2OLogger(verbose) << "Paddle model parsing failed." << std::endl;
    return false;
  }
  paddle2onnx::ModelExporter me;

  // Add custom operator information
  if (ops != nullptr && op_count > 0) {
    for (int i = 0; i < op_count; ++i) {
      std::string op_name(ops[i].op_name, strlen(ops[i].op_name));
      std::string export_op_name(ops[i].export_op_name,
                                 strlen(ops[i].export_op_name));
      if (export_op_name == "paddle2onnx_null") {
        export_op_name = op_name;
      }
      me.custom_ops[op_name] = export_op_name;
    }
  }
  std::string calibration_str;
  std::string result = me.Run(
      parser, opset_version, auto_upgrade_opset, verbose, enable_onnx_checker,
      enable_experimental_op, enable_optimize, deploy_backend, &calibration_str,
      external_file, save_external, export_fp16_model);
  if (result.empty()) {
    P2OLogger(verbose) << "The exported ONNX model is invalid!" << std::endl;
    return false;
  }

  if (parser.is_quantized_model && "tensorrt" == std::string(deploy_backend) &&
      calibration_str.empty()) {
    P2OLogger(verbose) << "Can not generate calibration cache for TensorRT "
                          "deploy backend when export quantize model."
                       << std::endl;
    return false;
  }
  *out_size = result.size();
  *out = new char[*out_size]();
  memcpy(*out, result.data(), *out_size);
  if (calibration_str.size()) {
    *calibration_size = calibration_str.size();
    *calibration_cache = new char[*calibration_size]();
    memcpy(*calibration_cache, calibration_str.data(), *calibration_size);
  }
  return true;
}

PADDLE2ONNX_DECL bool ConvertFP32ToFP16(const char* onnx_model, int model_size,
                                        char** out_model, int* out_model_size) {
  std::string onnx_proto(onnx_model, onnx_model + model_size);
  ONNX_NAMESPACE::ModelProto model;
  model.ParseFromString(onnx_proto);

  P2OLogger(true) << "Convert FP32 ONNX model to FP16." << std::endl;
  ConvertFp32ToFp16 convert;
  convert.Convert(&model);
  // save external data file for big model
  std::string external_data_file;
  if (model.ByteSizeLong() > INT_MAX) {
    external_data_file = "external_data";
  }
  paddle2onnx::ModelExporter me;
  if (external_data_file.size()) {
    me.SaveExternalData(model.mutable_graph(), external_data_file);
  }
  // check model
  me.ONNXChecker(model, true);

  std::string result;
  if (!model.SerializeToString(&result)) {
    P2OLogger(true)
        << "Error happenedd while optimizing the exported ONNX model."
        << std::endl;
    return false;
  }

  *out_model_size = result.size();
  *out_model = new char[*out_model_size]();
  memcpy(*out_model, result.data(), *out_model_size);
  return true;
}

ModelTensorInfo::~ModelTensorInfo() {
  if (shape != nullptr) {
    delete[] shape;
    shape = nullptr;
    rank = 0;
  }
}
}  // namespace paddle2onnx